Semantic segmentation algorithm for spacer bar installation robot to recognize installation gap
Aiming at the problems of spacer bar mounting robot's operation process,such as the spacer bar mounting gap rec-ognition accuracy is not high enough to affect the spacer bar localization,and the slow segmentation speed is not favorable for the deployment on mobile embedded devices,a lightweight spacer bar mounting robot mounting gap recognition algorithm based on the improved DeepLabV3+is proposed.The lightweight MobileNetV3 network is used to reduce the number of parameters and compu-tation;the ASPP is first downscaled and then upscaled to reduce the number of parameters of the model while guaranteeing the seg-mentation accuracy of the model;and the ECA attention mechanism is introduced to obtain more accurate target boundaries;the combined Loss function of Focal Loss and Dice Loss was used to enhance the segmentation effect of the installation gap of spacer rod.The experimental results show that the MIoU and MPA of the improved DeepLabV3+algorithm are improved by 4.25 and 1.85 percentage point,respectively,and the prediction speed is improved by 22.47%.The model trained by the improved algorithm is de-ployed to the robot for experiments,and the spacer bar mounting gaps identified are in line with the field experimental process,which verifies the effectiveness of the improved algorithm.
spacer rod installation robotinstallation gapimproved deeplabv3+lightweightattention mechanism
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